L. M. Knott, E. Long, C. P. Garner, A. Fly, B. Reid, A. Atkins
{"title":"Insights Into Lithium-Ion Battery Cell Temperature and State of Charge Using Dynamic Electrochemical Impedance Spectroscopy","authors":"L. M. Knott, E. Long, C. P. Garner, A. Fly, B. Reid, A. Atkins","doi":"10.1155/2024/9657360","DOIUrl":null,"url":null,"abstract":"<div>\n <p>Understanding and accurately determining battery cell properties is crucial for assessing battery capabilities. Electrochemical impedance spectroscopy (EIS) is commonly employed to evaluate these properties, typically under controlled laboratory conditions with steady-state measurements. Traditional steady-state EIS (SSEIS) requires the battery to be at rest to ensure a linear response. However, real-world applications, such as electric vehicles (EVs), expose batteries to varying states of charge (SOC) and temperature fluctuations, often occurring simultaneously. This study investigates the impact of SOC and temperature on EIS in terms of battery properties and impedance. Initially, SSEIS results were compared with dynamic EIS (DEIS) outcomes after a full charge under changing temperatures. Subsequently, DEIS was analysed using combined SOC and temperature variations during active charging. The study employed a commercial 450 mAh lithium-ion (Li-ion) cobalt oxide (LCO) graphite pouch cell, subject to a 1C constant current (CC)–constant voltage (CCCV) charge for SSEIS and CC charge for DEIS, with SOC ranging from 50% to 100% and cell temperatures from 10 to 35°C. The research developed models to interpolate battery impedance data, demonstrating accurate impedance predictions across operating conditions. Findings revealed significant differences between dynamic data and steady-state results, with DEIS more accurately reflecting real-use scenarios where the battery is not at equilibrium and exhibits concentration gradients. These models have potential applications in battery management systems (BMSs) for EVs, enabling health assessments by predicting resistance and capacitance changes, thereby ensuring battery cells’ longevity and optimal performance.</p>\n </div>","PeriodicalId":14051,"journal":{"name":"International Journal of Energy Research","volume":"2024 1","pages":""},"PeriodicalIF":4.3000,"publicationDate":"2024-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/2024/9657360","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Energy Research","FirstCategoryId":"5","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1155/2024/9657360","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Understanding and accurately determining battery cell properties is crucial for assessing battery capabilities. Electrochemical impedance spectroscopy (EIS) is commonly employed to evaluate these properties, typically under controlled laboratory conditions with steady-state measurements. Traditional steady-state EIS (SSEIS) requires the battery to be at rest to ensure a linear response. However, real-world applications, such as electric vehicles (EVs), expose batteries to varying states of charge (SOC) and temperature fluctuations, often occurring simultaneously. This study investigates the impact of SOC and temperature on EIS in terms of battery properties and impedance. Initially, SSEIS results were compared with dynamic EIS (DEIS) outcomes after a full charge under changing temperatures. Subsequently, DEIS was analysed using combined SOC and temperature variations during active charging. The study employed a commercial 450 mAh lithium-ion (Li-ion) cobalt oxide (LCO) graphite pouch cell, subject to a 1C constant current (CC)–constant voltage (CCCV) charge for SSEIS and CC charge for DEIS, with SOC ranging from 50% to 100% and cell temperatures from 10 to 35°C. The research developed models to interpolate battery impedance data, demonstrating accurate impedance predictions across operating conditions. Findings revealed significant differences between dynamic data and steady-state results, with DEIS more accurately reflecting real-use scenarios where the battery is not at equilibrium and exhibits concentration gradients. These models have potential applications in battery management systems (BMSs) for EVs, enabling health assessments by predicting resistance and capacitance changes, thereby ensuring battery cells’ longevity and optimal performance.
期刊介绍:
The International Journal of Energy Research (IJER) is dedicated to providing a multidisciplinary, unique platform for researchers, scientists, engineers, technology developers, planners, and policy makers to present their research results and findings in a compelling manner on novel energy systems and applications. IJER covers the entire spectrum of energy from production to conversion, conservation, management, systems, technologies, etc. We encourage papers submissions aiming at better efficiency, cost improvements, more effective resource use, improved design and analysis, reduced environmental impact, and hence leading to better sustainability.
IJER is concerned with the development and exploitation of both advanced traditional and new energy sources, systems, technologies and applications. Interdisciplinary subjects in the area of novel energy systems and applications are also encouraged. High-quality research papers are solicited in, but are not limited to, the following areas with innovative and novel contents:
-Biofuels and alternatives
-Carbon capturing and storage technologies
-Clean coal technologies
-Energy conversion, conservation and management
-Energy storage
-Energy systems
-Hybrid/combined/integrated energy systems for multi-generation
-Hydrogen energy and fuel cells
-Hydrogen production technologies
-Micro- and nano-energy systems and technologies
-Nuclear energy
-Renewable energies (e.g. geothermal, solar, wind, hydro, tidal, wave, biomass)
-Smart energy system